

import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__						 =			logging.get_logger(__name__)


lowerCAmelCase__						 =			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all BART models at https://huggingface.co/models?filter=bart
lowerCAmelCase__						 =			{
    """vocab_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
    },
}

lowerCAmelCase__						 =			{
    """facebook/bart-base""": 1_0_2_4,
    """facebook/bart-large""": 1_0_2_4,
    """facebook/bart-large-mnli""": 1_0_2_4,
    """facebook/bart-large-cnn""": 1_0_2_4,
    """facebook/bart-large-xsum""": 1_0_2_4,
    """yjernite/bart_eli5""": 1_0_2_4,
}



@lru_cache()
def        lowerCAmelCase__				(						)      ->   Optional[Any]:




				'''simple docstring'''





				A__          =      (
				    list(range(ord("!"   )						,							ord("~"   ) + 1   )   ) + list(range(ord("¡"   )						,							ord("¬"   ) + 1   )   ) + list(range(ord("®"   )						,							ord("ÿ"   ) + 1   )   )
				)
				A__          =      bs[:]
				A__          =      0
				for b in range(2**8   ):
								if b not in bs:
												bs.append(SCREAMING_SNAKE_CASE_   )
												cs.append(2**8 + n   )
												n += 1
				A__          =      [chr(SCREAMING_SNAKE_CASE_   ) for n in cs]
				return dict(zip(SCREAMING_SNAKE_CASE_						,							SCREAMING_SNAKE_CASE_   )   )



def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				Optional[Any]   )      ->   int:




				'''simple docstring'''





				A__          =      set()
				A__          =      word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)   )
								A__          =      char
				return pairs





class        a__    (       snake_case_      ):
			"""simple docstring"""







			__lowerCamelCase										=							VOCAB_FILES_NAMES
			__lowerCamelCase										=							PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase										=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase										=							["""input_ids""", """attention_mask"""]


			def __init__(     self   ,   lowercase   ,   lowercase   ,   lowercase="replace"   ,   lowercase="<s>"   ,   lowercase="</s>"   ,   lowercase="</s>"   ,   lowercase="<s>"   ,   lowercase="<unk>"   ,   lowercase="<pad>"   ,   lowercase="<mask>"   ,   lowercase=False   ,   **lowercase   ,   )	->    int:
							'''simple docstring'''






							A__          =      AddedToken(lowercase   ,   lstrip=lowercase   ,   rstrip=lowercase							) if isinstance(lowercase   ,   lowercase							) else bos_token
							A__          =      AddedToken(lowercase   ,   lstrip=lowercase   ,   rstrip=lowercase							) if isinstance(lowercase   ,   lowercase							) else eos_token
							A__          =      AddedToken(lowercase   ,   lstrip=lowercase   ,   rstrip=lowercase							) if isinstance(lowercase   ,   lowercase							) else sep_token
							A__          =      AddedToken(lowercase   ,   lstrip=lowercase   ,   rstrip=lowercase							) if isinstance(lowercase   ,   lowercase							) else cls_token
							A__          =      AddedToken(lowercase   ,   lstrip=lowercase   ,   rstrip=lowercase							) if isinstance(lowercase   ,   lowercase							) else unk_token
							A__          =      AddedToken(lowercase   ,   lstrip=lowercase   ,   rstrip=lowercase							) if isinstance(lowercase   ,   lowercase							) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							A__          =      AddedToken(lowercase   ,   lstrip=lowercase   ,   rstrip=lowercase							) if isinstance(lowercase   ,   lowercase							) else mask_token

							super().__init__(
							    errors=lowercase   ,   bos_token=lowercase   ,   eos_token=lowercase   ,   unk_token=lowercase   ,   sep_token=lowercase   ,   cls_token=lowercase   ,   pad_token=lowercase   ,   mask_token=lowercase   ,   add_prefix_space=lowercase   ,   **lowercase   ,   )

							with open(lowercase   ,   encoding="utf-8"							) as vocab_handle:
											A__          =      json.load(lowercase							)
							A__          =      {v: k for k, v in self.encoder.items()}
							A__          =      errors  # how to handle errors in decoding
							A__          =      bytes_to_unicode()
							A__          =      {v: k for k, v in self.byte_encoder.items()}
							with open(lowercase   ,   encoding="utf-8"							) as merges_handle:
											A__          =      merges_handle.read().split("\n"							)[1:-1]
							A__          =      [tuple(merge.split()							) for merge in bpe_merges]
							A__          =      dict(zip(lowercase   ,   range(len(lowercase							)							)							)							)
							A__          =      {}
							A__          =      add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							A__          =      re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"							)


			@property
			def   UpperCamelCase		(     self							)	->    int:
							'''simple docstring'''






							return len(self.encoder							)


			def   UpperCamelCase		(     self							)	->    Optional[Any]:
							'''simple docstring'''






							return dict(self.encoder   ,   **self.added_tokens_encoder							)


			def   UpperCamelCase		(     self   ,   lowercase							)	->    int:
							'''simple docstring'''






							if token in self.cache:
											return self.cache[token]
							A__          =      tuple(lowercase							)
							A__          =      get_pairs(lowercase							)

							if not pairs:
											return token

							while True:
											A__          =      min(lowercase   ,   key=lambda lowercase							: self.bpe_ranks.get(lowercase   ,   float("inf"							)							)							)
											if bigram not in self.bpe_ranks:
															break
											A__     ,					A__          =      bigram
											A__          =      []
											A__          =      0
											while i < len(lowercase							):
															try:
																			A__          =      word.index(lowercase   ,   lowercase							)
															except ValueError:
																			new_word.extend(word[i:]							)
																			break
															else:
																			new_word.extend(word[i:j]							)
																			A__          =      j

															if word[i] == first and i < len(lowercase							) - 1 and word[i + 1] == second:
																			new_word.append(first + second							)
																			i += 2
															else:
																			new_word.append(word[i]							)
																			i += 1
											A__          =      tuple(lowercase							)
											A__          =      new_word
											if len(lowercase							) == 1:
															break
											else:
															A__          =      get_pairs(lowercase							)
							A__          =      " ".join(lowercase							)
							A__          =      word
							return word


			def   UpperCamelCase		(     self   ,   lowercase							)	->    str:
							'''simple docstring'''






							A__          =      []
							for token in re.findall(self.pat   ,   lowercase							):
											A__          =      "".join(
											    self.byte_encoder[b] for b in token.encode("utf-8"							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
											bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase							).split(" "							)							)
							return bpe_tokens


			def   UpperCamelCase		(     self   ,   lowercase							)	->    str:
							'''simple docstring'''






							return self.encoder.get(lowercase   ,   self.encoder.get(self.unk_token							)							)


			def   UpperCamelCase		(     self   ,   lowercase							)	->    Optional[int]:
							'''simple docstring'''






							return self.decoder.get(lowercase							)


			def   UpperCamelCase		(     self   ,   lowercase							)	->    Optional[Any]:
							'''simple docstring'''






							A__          =      "".join(lowercase							)
							A__          =      bytearray([self.byte_decoder[c] for c in text]							).decode("utf-8"   ,   errors=self.errors							)
							return text


			def   UpperCamelCase		(     self   ,   lowercase   ,   lowercase = None							)	->    Tuple:
							'''simple docstring'''






							if not os.path.isdir(lowercase							):
											logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
											return
							A__          =      os.path.join(
							    lowercase   ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)
							A__          =      os.path.join(
							    lowercase   ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]							)

							with open(lowercase   ,   "w"   ,   encoding="utf-8"							) as f:
											f.write(json.dumps(self.encoder   ,   indent=2   ,   sort_keys=lowercase   ,   ensure_ascii=lowercase							) + "\n"							)

							A__          =      0
							with open(lowercase   ,   "w"   ,   encoding="utf-8"							) as writer:
											writer.write("#version: 0.2\n"							)
											for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   ,   key=lambda lowercase							: kv[1]							):
															if index != token_index:
																			logger.warning(
																			    F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																			    " Please check that the tokenizer is not corrupted!"							)
																			A__          =      token_index
															writer.write(" ".join(lowercase							) + "\n"							)
															index += 1

							return vocab_file, merge_file


			def   UpperCamelCase		(     self   ,   lowercase   ,   lowercase = None							)	->    int:
							'''simple docstring'''






							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							A__          =      [self.cls_token_id]
							A__          =      [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep


			def   UpperCamelCase		(     self   ,   lowercase   ,   lowercase = None   ,   lowercase = False							)	->    Any:
							'''simple docstring'''






							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowercase   ,   token_ids_a=lowercase   ,   already_has_special_tokens=lowercase							)

							if token_ids_a is None:
											return [1] + ([0] * len(lowercase							)) + [1]
							return [1] + ([0] * len(lowercase							)) + [1, 1] + ([0] * len(lowercase							)) + [1]


			def   UpperCamelCase		(     self   ,   lowercase   ,   lowercase = None							)	->    int:
							'''simple docstring'''






							A__          =      [self.sep_token_id]
							A__          =      [self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep							) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]


			def   UpperCamelCase		(     self   ,   lowercase   ,   lowercase=False   ,   **lowercase							)	->    List[Any]:
							'''simple docstring'''






							A__          =      kwargs.pop("add_prefix_space"   ,   self.add_prefix_space							)
							if (is_split_into_words or add_prefix_space) and (len(lowercase							) > 0 and not text[0].isspace()):
											A__          =      " " + text
							return (text, kwargs)


"""simple docstring"""



import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory



def 	_lowerCamelCase(	):
       raise RuntimeError("CUDA out of memory."				)






class  snake_case__      (       nn.Module       ):


 def __init__( self						):
        super().__init__()
        __a       =    nn.Linear(3    ,		4						)
        __a       =    nn.BatchNormad(4						)
        __a       =    nn.Linear(4    ,		5						)






 def 						a__   ( self    ,		lowerCamelCase						):
        return self.lineara(self.batchnorm(self.lineara(lowerCamelCase						)						)						)






class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        __a       =    []

        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase						):
               nonlocal batch_sizes
               batch_sizes.append(lowerCamelCase						)
               if batch_size != 8:
                      raise_fake_out_of_memory()

        mock_training_loop_function()
        self.assertListEqual(lowerCamelCase    ,		[128, 64, 32, 16, 8]						)


 def 						a__   ( self						):
        __a       =    []

        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase    ,		lowerCamelCase						):
               nonlocal batch_sizes
               batch_sizes.append(lowerCamelCase						)
               if batch_size != 8:
                      raise_fake_out_of_memory()
               return batch_size, arga

        __a ,   __a       =    mock_training_loop_function("hello"						)
        self.assertListEqual(lowerCamelCase    ,		[128, 64, 32, 16, 8]						)
        self.assertListEqual([bs, arga]    ,		[8, "hello"]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=0						)
        def mock_training_loop_function(lowerCamelCase						):
               pass

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("No executable batch size found, reached zero."    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=16						)
        def mock_training_loop_function(lowerCamelCase						):
               if batch_size > 0:
                      raise_fake_out_of_memory()
               pass

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("No executable batch size found, reached zero."    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=128						)
        def mock_training_loop_function(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
               if batch_size != 8:
                      raise raise_fake_out_of_memory()

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function(128    ,		"hello"    ,		"world"						)
               self.assertIn("Batch size was passed into `f`"    ,		cm.exception.args[0]						)
               self.assertIn("`f(arg1='hello', arg2='world')"    ,		cm.exception.args[0]						)


 def 						a__   ( self						):
        @find_executable_batch_size(starting_batch_size=16						)
        def mock_training_loop_function(lowerCamelCase						):
               raise ValueError("Oops, we had an error!"						)

        with self.assertRaises(lowerCamelCase						) as cm:
               mock_training_loop_function()
               self.assertIn("Oops, we had an error!"    ,		cm.exception.args[0]						)






 @require_cuda
 def 						a__   ( self						):
        __a       =    torch.cuda.memory_allocated()
        __a       =    ModelForTest()
        model.cuda()
        self.assertGreater(torch.cuda.memory_allocated()    ,		lowerCamelCase						)
        __a       =    release_memory(lowerCamelCase						)
        self.assertEqual(torch.cuda.memory_allocated()    ,		lowerCamelCase						)
